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A Layman's Guide to Data Science Workflow
Published
25th Apr, 2024
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When you get involved in a data science project, you must always take care of basic elements first before starting a project like business objective, domain knowledge, standard data science practices of an organization, and previous experiences while considering the next steps to problem solutions like data source identification, data modeling, data management, and data visualizations.
The data science industry already offers a variety of data science workflow frameworks to solve different kinds of data science problems. It is not possible to develop an all-inclusive Data Science Workflow to solve all business problems. In lieu of that, it is important to follow some best-standard data science practices, such as automating data pipelines, planning inferences, and doing a post-mortem at the end of every project to identify any potential improvement areas.
You will learn about various standard data science workflows in this article. You will also gain an understanding of the structure of a Data Science Workflow and the considerations that need to be taken into account as you follow the Data Science Workflow. Discover what Data Science workflow is in this article. Technical implementation of data science workflow implementation requires a data scientist to be skilled in python, and data science, statistics, and data modeling.
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What is a Data Science Workflow?
A workflow is a systematic sequence of tasks a person performs to complete a project. Solving a real-world data science problem is complex in nature where you have to take care of multiple scenarios from defining the data science problem to deployment and value realization to get to solve the data science problem in the right way. A well-defined data science workflow defines the steps required to complete the data science project successfully. It helps the data science team to track progress, avoid confusion among them, understand the reasons for the delay, and know the expected timeline of implementation of a data science project. In a sense, a well-defined data science workflow acts as a set of guidelines for planning, organizing, and implementing data science projects. A typical data science workflow diagram shown below in the figure, generally contains six steps Problem/business case identification, data collection and storage, data wrangling, data analysis and modeling, evaluation and inference, and deployment respectively. You should note, however, that the process outlined below is not linear. The majority of Data Science projects are iterative, requiring multiple stages to be repeated and revisited.
Figure 1: A typical data science workflow diagram
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What Are the Steps in a Data Science Workflow?
Data science problems bring a variety of problems so it’s understandable there is no concrete workflow design that can be fit for all types of data science projects. Mature data science defines their own workflow structure that works for their team and they tweak elements in data science workflow to get to the solution.
We often see the common workflow when approaching different data science problems, irrespective of the dataset. Let's focus on this workflow.
Step 1: Data Science Problem Framework Phase
It may sound simple to define a data science problem, but it is not so easy to ensure that it is the correct problem because it is always influenced by organization and business needs. Identifying and stating the problem clearly is the most important step in any Data Science project. This step sets objectives and guides the rest of your data science project and team.
Better problem definition keeps stakeholders' expectations in check, reduces unnecessary iterations, and creates a better understanding of the product for developers, analysts, and data scientists. An individual who speaks the language of both data and business is extremely useful during this process, as they serve as a link between business and data science teams, and they are the ideal person to enforce certain principles during the problem definition process.
Some of the steps you and your team must take better problem definition process:
- People with both business and data acumen should be involved in problem definition.
- Leadership should allow time for a rigorous definition of the problem.
- Analyze the problem in terms of data complexity, data availability, and data liability.
- The goal should be to define the problem clearly and to solve it in a way that will benefit the business.
Step 2: Data Acquisition Phase
In Data Science, you cannot get the result without good quality data. Getting the right quality of data from multiple sources is one of the most important steps in your data science workflow, and you will be spending 60% to 70% of your time gathering the right quality of data. It is necessary to gather all the relevant data, format it into a form that can be analyzed, and clean it before analyzing it.
Data Collection
In order to accomplish a project or solve a problem, you must collect the data that will fuel it. Data collection is one of the important tasks in your data science workflow and getting it right is essential for the existence of the whole project. You must identify the various sources from which you can gather the data.
There are several sources.
- CSV files on your local machine, Google Spreadsheets, or Excel
- Data obtained from SQL servers
- Retrieved data from public websites and online repositories
- Online content delivered via an API
- An IOT data source
- Data from software logs, such as a web server
- Data stored in cloud such as an AWS, Azure, or GCP
When gathering data, it can be a messy procedure, especially if the data does not come from a well-organized source. Working with multiple sources and employing a variety of tools and methodologies will be required when compiling a dataset.
Please keep the following points in mind while the data collection process:
1) Data Provenance
The process of tracing and recording the origins of data and its movement among databases is known as data provenance. Provenance is becoming increasingly important in scientific databases, where it is crucial for data validation in later stages.
2) Data Management and versioning
When a company creates or downloads data files, it is critical to give those files proper names and organize them into directories to avoid duplication and version confusion. When new versions of those files are created, the names of all versions of those files should be corresponding in order to track their differences.
3) Data Storage
Because the amount of data accessed in a day is practically limitless, it is frequently saved on remote servers because a hard disc can't keep it all. Despite the rise of cloud services, a large portion of data analysis is still done on desktop computers using data sets that fit on current hard drives (i.e., less than a terabyte).
4) Data Reformatting & Cleaning
Data that has been produced by someone else without considering the analysis is not always in an easy to analyze format. Furthermore, raw data frequently contains grammatical errors, missing entries, and irregular forms, necessitating its "cleaning" prior to analysis.
5) Data Wrangling
This entails cleaning your data, arranging it into a workspace, and ensuring that it is error-free. The data can be reformatted and cleaned manually or via the use of programs. Converting integers to floats, for example, may be necessary in some circumstances to receive all of the data in the right format. After that, a solution for the null and missing values that characterize sparse matrices must be discovered.
Step 3: Data Exploration
When the Data Scientists must spend time familiarizing themselves with it. It is critical to generate hypotheses during this phase while they look for pattern and abnormalities in the data. To start with, establish whether the problem statement hints of supervised or unsupervised approach. Do you have to deal with regression or classification? Is it our intention to infer or visualize or forecast a constraint?
- Supervised Learning Includes constructing a model using examples of input-output pairings in order to understand how an input translates to an output.
- Unsupervised Learning finds trends in unlabeled data by constructing a model from the data.
- A Classification is a Supervised Learning model that helps you classify the unlabeled data into a particular category which can be binary or multi-class
- Regression is a supervised learning model which helps in predicting continuous variables from unknown data.
During this phase, you should try to understand the data so that you can develop hypotheses that can be tested once you get to data modelling, the next step in the workflow.
Step 4: Data Modeling
When you have analyzed and understood the model you might build whether it will be classification or regression or a different kind of problem. Because of the nature of Data Science, it will almost certainly be necessary to evaluate a wide range of ideas before determining how to proceed. There are three steps to this:
- Learning and generalizing a Machine Learning algorithm based on training data is required.
- The fitting step entails determining if the machine learning model can generalize to previously unseen examples comparable to the data on which it was trained.
- Validation is the process of evaluating a trained model against data that differs from the training data.
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Step 5: Reflection or Inference Phase
Typically, data scientists cycle between analysis and reflection phases: analysis focuses on coding, while reflection focuses on thinking about and sharing analytical results. Data scientists or teams of data scientists may explore script options and settings by comparing output variations and evaluating a series of results.
Data analysis is fundamentally a trial-and-error process: a scientist runs tests, graphs the findings, runs the tests again, graphs the results, and so on. Because graphs may be presented side by side on displays, they can be used to visually compare and contrast their qualities.
Step 6: Communicating & Visualizing Results
The first few years of a Data Scientist's career are often spent worrying about Machine Learning algorithms. After some time, however, these same people begin to realize that soft skills should be their focus.
It is important for Data Scientists to be able to effectively communicate their findings because they will be doing so frequently. A Data Scientist is responsible for presenting findings, conclusions, and stories to various stakeholders during this phase. Due to the fact that these stakeholders don't have much knowledge about Data Science, adapting their message through appealing visualizations will contribute to their understanding.
Comparing the Different Workflows
It's not unexpected that there are a lot of blog articles where people discuss their personal workflow. I have gone through some of them and I think it might help you understand how flexible a data science workflow is; different organizations or people restructure them in accordance with their needs.
All of the available frameworks generally concentrate on the steps in a data science project (or skills needed by a data scientist). The most notable distinction is that some openly explain the necessity to return to a prior phase. Another distinction is that some concentrate on understanding the business environment, while others concentrate on model implementation. what are the parallels and distinctions between these workflow frameworks?
To help you examine the various workflows, the table below examines the different stages specified under each workflow framework. This table may assist you in determining which stages are suitable for your team and if you want to adopt one of the previously created processes or construct your own, depending on the phases that make the most sense for your team.
Parameters | |||||
Problem Framework | Ask an interesting question | Business understanding |
| Framing the problem and the goals
|
|
Data Aquisition | Get the data | Data understanding | Obtain | Prepare (acquire) | Fetch |
Data Cleaning |
| Data preparation | Scrub | Prepare (clean) | Clean and prepare |
Data Exploration | Explore the data |
| Explore | Explore |
|
Model | Model the data | Modeling | Model | Analysis | Train model |
Model Evaluation |
| Evaluation | Interpret | Reflect/Performance metric | Evaluate Model |
Communicate | Communicate / Visualize |
|
| Communicate/Visualize/stakeholder meetings |
|
Model Deployment |
| Deployment |
|
| Deploy to production |
Performance Monitoring |
|
|
|
| Monitor/collect data/evaluate |
Conclusion
I hope this article has given you a basic understanding of data science workflow. Because data science procedures are iterative in nature, reproducibility is critical to their success. Furthermore, data science is a collaborative endeavor. You should examine how your team members work and develop a set of practices that are tailored to your team's values and goals in order to define your team's workflow. It can also be beneficial to evaluate current frameworks and determine what can be learned from them.
Frequently Asked Questions (FAQs)
1. What are the four data science workflows?
There are multiple data science workflows currently in use at various data science organizations. Four data science workflows that are impactful are; AWS Sagemaker, CRISP-DM, Sciforce’s, and Harvard, Sciforce’s, and Harvard.
2. What are the five steps of data science?
Five steps in data science workflows are; Problem Framework, Data Acquisition and Cleaning, Data Exploration, Model Development, and Model Evaluation.
3. What is the first step in data science workflow?
The first step in data science workflow in most cases is Data Acquisition because without data you can solve a data science problem.
4. What are the tools used in data science?
Some of the popular tools used in data science are Scikit-learn, Tensorflow, Pytorch, SAS, Jupyter, Excel, Power BI, Apache Spark , BigML, MATLAB, etc.
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